): Screen a large number of factors (e.g., impeller speed, solids concentration, gas holdup) to identify which ones significantly impact recovery.
Identifies data points that fall beyond a specific number of standard deviations from the mean.
(such as Kriging) allows engineers to interpolate data between drill holes, creating a 3D model of the resource that dictates the entire mine plan. 2. Design of Experiments (DoE)
Occurs when particles are incorrectly excluded or included by the sampler head due to bouncing or splashing. Statistical Methods For Mineral Engineers
) as a function of ore hardness (Bond Work Index), feed size ( F80cap F sub 80 ), and mill power draw:
Statistical methods are essential for mineral engineers to manage the inherent variability in geological materials and processing plant performance. These tools enable data-driven decisions during exploration, ore characterization, and plant optimization.
Dispersion metrics quantify process stability. Variance and standard deviation measure the spread of data around the mean. A high standard deviation in flotation feed grade signals unpredictable mineralogy, which requires immediate operator intervention. Probability Distributions in Mining ): Screen a large number of factors (e
Modern mineral processing plants generate thousands of data points every second via SCADA systems and online analyzers (e.g., courier XRF systems). Univariate statistics cannot handle this scale. Principal Component Analysis (PCA)
Statistics provides the tools to quantify those errors and act on signal, not noise.
Control charts plot process data over time relative to calculated statistical limits, differentiating between common-cause variation (normal system noise) and special-cause variation (identifiable operational faults). not poor analysis.
Monitoring daily recovery, grind sizes, and thickener underflow
Following optimization, maintains that performance. Every process exhibits two types of variation: common cause (inherent, stable noise) and special cause (assignable to a specific event like a bin blockage or a sensor failure). Using control charts (e.g., X-bar and R charts), an engineer monitors key performance indicators (KPIs) such as concentrate grade or tailings recovery. When a data point falls outside the statistically calculated control limits, it signals that the process is likely out of control and requires investigation. SPC acts as an early warning system, preventing off-spec product or excessive metal loss before it occurs, shifting the engineer’s role from reactive firefighting to proactive management.
Over 50% of plant metallurgical balance errors originate from poor sampling, not poor analysis.